Pith. sign in

REVIEW 4 major objections 6 minor 67 references

HaGRIDv2: 1M Images for Static and Dynamic Hand Gesture Recognition

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read HaGRIDv2, a 1,086,158-image hand gesture dataset with 33 gesture classes plus a diversified 'no gesture' class, claims the best cross-dataset generalization among gesture detection sets while cutting false positives sixfold versus its…

desk verdict A genuinely useful large-scale gesture dataset, but the unaudited auto-labeling pipeline and unevaluated dynamic algorithm mean the headline numbers should be read with caution. read the letter →

arxiv 2412.01508 v1 pith:3NBZIB63 submitted 2024-12-02 cs.CV

Anton Nuzhdin , Alexander Nagaev , Alexander Sautin , Alexander Kapitanov , Karina Kvanchiani This is my paper · ORCID
classification cs.CV
keywords handgesturerecognitiondatasetconstructionstaticgesturesdynamicdomaingeneralizationfalsepositivespre-traininggeneration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces HaGRIDv2, an expanded version of the HaGRID hand-gesture dataset, adding 531,358 new images across 15 new static gesture classes, including two-handed ones, and a diversified 'no gesture' class of natural hand movements. The authors claim this combination, totalling 1,086,158 images across 33 gesture classes plus 'no gesture', yields the best domain generalization among gesture detection datasets when evaluated cross-dataset, and reduces false positives by six times relative to the original HaGRID. They also extend HaGRID's dynamic gesture recognition algorithm with new manipulative gestures (swipes, clicks, zooms, drag-and-drop) built from static frames, and report improved pre-training for gesture-related tasks and improved gesture fidelity in diffusion-model generation. A sympathetic reader would take the paper's core assertion to be that a larger, more diverse static gesture vocabulary, paired with a realistic 'no gesture' set, is the key ingredient for robust and transferable gesture recognition.

What carries the argument

The load-bearing object is the dataset itself, constructed through a pipeline that reuses HaGRID's crowdsourcing protocol but replaces manual box annotation with a YOLOv10x detector trained on HaGRID hand boxes, followed by crowd moderation; two-handed gestures get merged joint boxes. The diversified 'no gesture' class and the frame-queue dynamic gesture algorithm — which classifies each frame's hand crop with a lightweight Residual Block and tracks gesture start and end over a 30-frame window — are the two mechanisms that carry the false-positive and dynamic-gesture claims.

What would settle it

Take a random sample of, say, 1,000 images from each of the 15 new gesture classes in HaGRIDv2, have annotators label hand boxes and gesture classes manually, and compare with the automated boxes and labels. If per-class IoU or label agreement is substantially lower for new classes than for the original HaGRID classes, then the reported cross-dataset gains and the sixfold false-positive reduction are not cleanly attributable to the dataset's content.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that scaling a static gesture dataset along three axes — more gesture classes (from 18 to 33), more subjects (from 37,583 to 65,977), and a 'no gesture' class that reflects natural hand behaviour during video calls — produces a training resource that transfers better to unseen datasets than existing gesture and hand detection sets. The sixfold reduction in false positives is attributed specifically to the diversified 'no gesture' class, while the cross-dataset results (Table 3) show HaGRIDv2-trained models achieving the highest average mAP when tested on other datasets. The authors further claim that this data, combined with a frame-queue classifier that recognizes gesture sequences from static crops, enables lightweight dynamic gesture recognition of manipulative gestures without video-based training.

Load-bearing premise

The entire dataset and every downstream result rests on the assumption that a YOLOv10x detector trained only on original HaGRID images produces correct hand boxes and labels for all 1,086,158 new images, including 15 newly added gesture classes and two-handed poses, with crowd moderation but no reported measurement of annotation accuracy.

Editorial extensions

If this is right

  • Models pre-trained on HaGRIDv2 beat models pre-trained on HaGRID when fine-tuned on HANDS and OUHANDS for gesture classification and detection.
  • A detector trained on HaGRIDv2 produces six times fewer false positives on realistic 'no gesture' frames than one trained on HaGRID.
  • The dynamic gesture algorithm recognizes swipes, clicks, zooms, and drag-and-drop using only static image training, running at 106.14 MFLOPs for detection and 6.9 MFLOPs for classification on a CPU.
  • HaGRIDv2-tuned Stable Diffusion 2.1 generates gestures that crowdworkers judge closer to the reference gesture than HaGRID-tuned or base models.
  • The dataset enables support for both conversational and control gestures in a single resource, covering the full spectrum needed for video-conference and home-automation systems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The generalization claim is bounded by the comparison set: only OUHANDS and HANDS for gesture detection, and EgoHands, BodyHands, Human-Parts plus HaGRID for hand detection, so 'best' is relative to a small and partly aging set of benchmarks.
  • The paper reports no quantitative check of automated annotation quality on the 15 new classes; without such a check, the measured improvements could partly reflect the annotation pipeline rather than the dataset's content.
  • The sixfold false-positive reduction is measured on a test set drawn from the same 'no gesture' distribution the authors designed; a harder test would use natural hand movements from other sources and contexts.
  • The frame-queue dynamic algorithm is deterministic and depends on exact gesture execution, so real-world robustness would likely require either a learned temporal model or tolerance thresholds; the paper acknowledges this as a limitation.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper presents HaGRIDv2, a large-scale hand-gesture recognition dataset that extends the authors' earlier HaGRID dataset with 15 new gesture classes and a more diverse "no gesture" class, totaling 1,086,158 images. The manuscript describes an automated annotation pipeline based on a YOLOv10x detector trained on HaGRID hand boxes with crowd moderation, reports detection and classification results on the new dataset, cross-dataset generalization experiments for gesture and hand detection, a comparison of HaGRIDv2 with HaGRID for pre-training and false-positive reduction, and an extension of a dynamic gesture recognition algorithm built on static gestures. The dataset, pre-trained models, and dynamic gesture code are promised to be publicly released.

Significance. If released as described, HaGRIDv2 would be a useful resource: it is substantially larger and more class-diverse than HaGRID, adds two-handed gestures, and provides a diversified "no gesture" class that directly targets the false-positive problem in real human-computer interaction deployments. The cross-dataset experiments and pre-training comparisons are a practical evaluation strategy, and the public release of data and models is a concrete contribution. However, the strongest quantitative claims (best cross-dataset generalization, 6x false-positive reduction, high detection mAP) all sit downstream of an unvalidated automatic annotation pipeline, and the dynamic gesture algorithm is presented without any quantitative evaluation. With an annotation audit and a quantitative dynamic-gesture benchmark, the contribution would be solid; in the current form the evidence is incomplete.

major comments (4)
  1. [Section 3.1] The automated annotation step is the foundation of every reported detection metric, but its accuracy is never measured. The YOLOv10x hand detector is trained only on HaGRID's one-handed classes and then used to label 15 new classes, two-handed gestures, and the expanded "no gesture" class, with the "hanging hand is below the gesture hand" rule for one-handed images and box-merging rules for two-handed images. No IoU against manual boxes, no per-class detection recall on the new classes, no label-agreement statistic for crowd moderation, and no description of disagreement resolution are reported. Because Tables 2-4 and Section 7.2 evaluate models against these same automatic labels, systematic noise (for example, missed hands in two-handed poses, mislabeled "no gesture" frames, or failures of the "below" heuristic when a non-gesturing hand is raised) propagates into every quantitative claim, including the 6x false-positive reduction. I ask for a stratified manual audit reporting IoU and per-class recall/precision of the automatic boxes, with explicit treatment of two-handed and "no gesture" images.
  2. [Section 4] The dynamic gesture recognition algorithm is a stated contribution, but the paper provides no quantitative evaluation of it. The only evidence is the pipeline description and demo screenshots in Fig. 5 and Fig. 11 of the supplementary material. There is no accuracy, F1, false-positive rate, latency, per-gesture breakdown, comparison with the previous algorithm [23], or ablation of the queue length n=30 and the duration/location checks. Without such measurements, the claim of an extended and functional dynamic-gesture system cannot be assessed. I request a quantitative evaluation on a held-out set of gesture sequences, or at minimum a user study with well-defined metrics, plus an ablation of the queue length and other algorithm parameters.
  3. [Section 6.1, Table 3] The statement that "diagonal values were excluded from the averages" is not consistent with the printed numbers. For the HaGRIDv2 row, the off-diagonal entries are 67 and 66.3, whose average is 66.7, yet the table reports 73.8; the same pattern holds for the OUHANDS and HANDS rows and for the "Test avg. mAP" row, whose values equal averages over all three entries including the diagonal. Additionally, the "best generalization ability" claim rests on only two accessible datasets and five overlapping gesture classes. Please recompute the averages or state that the diagonal is included, and either add per-class results and error bars or soften the wording to "best among the compared datasets."
  4. [Section 7.2] The central claim of a 6x false-positive reduction is based on one YOLOv10n training run per dataset and one test set, with no repeated seeds, confidence intervals, or significance testing. Combined with the fact that the test labels come from the unvalidated automatic pipeline discussed in Major Comment 1, the quantitative magnitude of this claim is fragile. Please report variance across training seeds and, ideally, re-evaluate on a manually verified subset of "no gesture" images.
minor comments (6)
  1. [Section 3.2] The text says "Fig. 2e-g shows the subjects' age, gender, and race distributions," but the caption of Fig. 2 places age, gender, and race in panels (g-i); please correct the cross-reference.
  2. [References and Section 7.1] The references are incomplete in places: [31] is cited as "arXiv preprint arXiv:****" with no identifier, and Section 7.1 refers to "HTU HGR" while Section 2.2 and reference [54] use "NTU HGR"; please unify.
  3. [Section 7.3] The SBS evaluation description is difficult to follow: "Each model generated three images per gesture, totaling 6 images per gesture" is ambiguous, and the numbers 162 and 234 comparisons are not derived. Please clarify the counting and report inter-annotator agreement.
  4. [Section 8] Section 8 says the dataset is released "under a public license for non-commercial use in research purposes," whereas the Introduction states a "modified Creative Commons CC-BY 4.0 license"; CC-BY 4.0 permits commercial use. Please reconcile these statements.
  5. [Section 4] The text calls the dynamic recognition approach "novel," but the preceding paragraph says it builds on the algorithm in [23]; please rephrase to avoid overclaiming novelty.
  6. [Section 5.2] The metrics are described as "remarkably high" without uncertainty estimates; reporting standard deviations or confidence intervals for the main detection and classification numbers would make the comparison more informative.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: the dataset metrics, generalization results, and false-positive comparison are empirical measurements, not reductions to fitted inputs; the missing annotation audit is a data-quality limitation, not circularity.

full rationale

The paper's main quantitative claims are standard empirical evaluations rather than identities forced by construction. In Section 3.1 the authors replace manual annotation with a YOLOv10x detector trained on HaGRID, and the resulting boxes are used as ground truth for later mAP and false-positive measurements; however, no reported metric is defined algebraically in terms of that detector's training outputs, so this is a data-quality risk (no IoU or per-class recall audit is reported) rather than a circular step. The dynamic gesture recognition algorithm in Section 4 extends the authors' prior method [23] with deterministic checks and a queue, but the claimed functionality is not derived as an equivalence from the prior algorithm. The Section 7.2 false-positive comparison trains two detectors on HaGRID and HaGRIDv2 and tests on HaGRIDv2 no-gesture samples; the 6x reduction is confounded by training-distribution overlap but remains a measured outcome, not a fitted parameter renamed as a prediction. Self-citations to HaGRID are natural for a dataset-extension paper and are supported by the publicly available original dataset and its external WACV publication. The Limitations section explicitly acknowledges robustness and bias concerns, but none of these constitute a reduction of a claimed result to its own input. No circular step can be exhibited with a specific equation or construction, so the circularity score is 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The dataset's performance claims rest on assumptions about annotation quality, representativeness of the no-gesture class, and the adequacy of a small class overlap for generalization comparisons. The only explicit free parameter is the queue length n=30 in the dynamic gesture algorithm.

free parameters (1)
  • queue length n = 30
    Set experimentally to 30 in the dynamic gesture recognition algorithm (Section 4); no ablation study is provided to justify this value.
assumptions (4)
  • domain assumption A YOLOv10x detector trained only on HaGRID hand boxes generalizes to accurately label all new HaGRIDv2 images, including 15 new gesture classes and two-handed poses.
    Invoked in Section 3.1 for automated annotation; no annotation quality metrics are reported to support this assumption.
  • domain assumption The five overlapping gesture classes (fist, one, palm, peace, three) are representative enough for cross-dataset generalization claims.
    Used in Section 6.1 to reduce all datasets to a five-class overlap and then to claim the best generalization ability.
  • domain assumption The natural hand positions selected for the "no gesture" class capture the distribution of false-positive triggers in production video meeting scenarios.
    Underpins the false-positive reduction claim in Section 7.2; the selection is based on the authors' domain judgment.
  • domain assumption Dynamic gestures can be recognized by classifying static frames and applying a rule-based queue over the last n frames, without video training.
    Core assumption of the dynamic gesture algorithm in Section 4; the paper provides no quantitative evidence that this assumption holds for the target gestures.

how reviews work

0 comments
Cite this review

Pith. "Pith review of HaGRIDv2: 1M Images for Static and Dynamic Hand Gesture Recognition." pith.science (2026). https://pith.science/paper/3NBZIB63

@misc{pith2026241201508,
  author       = {Pith},
  title        = {Pith review of: HaGRIDv2: 1M Images for Static and Dynamic Hand Gesture Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3NBZIB63}},
  note         = {Machine review of arXiv:2412.01508}
}
read the original abstract

This paper proposes the second version of the widespread Hand Gesture Recognition dataset HaGRID -- HaGRIDv2. We cover 15 new gestures with conversation and control functions, including two-handed ones. Building on the foundational concepts proposed by HaGRID's authors, we implemented the dynamic gesture recognition algorithm and further enhanced it by adding three new groups of manipulation gestures. The ``no gesture" class was diversified by adding samples of natural hand movements, which allowed us to minimize false positives by 6 times. Combining extra samples with HaGRID, the received version outperforms the original in pre-training models for gesture-related tasks. Besides, we achieved the best generalization ability among gesture and hand detection datasets. In addition, the second version enhances the quality of the gestures generated by the diffusion model. HaGRIDv2, pre-trained models, and a dynamic gesture recognition algorithm are publicly available.

Figures

Figures reproduced from arXiv: 2412.01508 by the authors.

Figure 1
Figure 1. The 15 outlined in red new gesture classes added to [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The key statistics of HaGRIDv2. (a) Image resolution distribution showing the scatter of image dimensions; (b-d) Distribution [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Impact of pre-training on gesture classification and de [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Comparing the false positives on the “no gesture” class [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: The screenshots from the dynamic gesture recognition demo. The bounding boxes highlight detected gestures with their class [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Different tasks addressed by HaGRIDv2. (a) The gesture detector aims to predict a bounding box with a label for each hand on [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: The SBS results compare Stable Diffusion 2.1 fine-tuned on the HaGRID and HaGRIDv2 datasets, as well as a comparison [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Samples of the “no gesture” class in HaGRID and HaGRIDv2 datasets. [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: The pipeline for automatic image annotation. a) The higher hand on the one-handed gesture image is marked as the gesticulating [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: Samples from HaGRIDv2 dataset [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: The algorithm for recognizing dynamic gestures, exemplified by the “zoom out” gesture. [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: Samples from datasets, participated in cross-dataset evaluation and pre-trains impact experiment. [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: The interface for SBS evaluation presenting two images generated by two Stable Diffusion 2.1 models fine-tuned on HaGRID [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]
Figure 14
Figure 14. Figure 14: Examples of generated images with three Stable Diffusion 2.1 models: original, fine-tuned on HaGRID, and fine-tuned on [PITH_FULL_IMAGE:figures/full_fig_p016_14.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

67 extracted references · 61 canonical work pages

  1. [23]

    Hagrid – hand gesture recognition image dataset

    Alexander Kapitanov, Karina Kvanchiani, Alexander Na- gaev, Roman Kraynov, and Andrei Makhliarchuk. Hagrid – hand gesture recognition image dataset. In Proceedings of the IEEE/CVF Winter Conference on Applications of Com- puter Vision (WACV), pages 4572–4581, January 2024

    work page 2024

  2. [1]

    https://workspaceupdates.googleblog.com/ 2023/11/google-meet-hand-raise-gesture- detection.html

    Raise your hand with gesture detection in google meet. https://workspaceupdates.googleblog.com/ 2023/11/google-meet-hand-raise-gesture- detection.html

    work page 2023

  3. [2]

    https : / / support

    Zoom gesture recognition. https : / / support . zoom.com/hc/en/article?id=zm_kb&sysparm_ article=KB0067842

    work page

  4. [3]

    https : //github.com/Linzaer/Ultra- Light- Fast- Generic-Face-Detector-1MB , 2019

    Ultra-light-fast-generic-face-detector. https : //github.com/Linzaer/Ultra- Light- Fast- Generic-Face-Detector-1MB , 2019

    work page 2019

  5. [4]

    https : / / users

    SHAPE Dataset. https : / / users . soict . hust . edu.vn/linhdt/dataset/, 2021

    work page 2021

  6. [5]

    Smart home automation-based hand gesture recogni- tion using feature fusion and recurrent neural network

    Bayan Alabdullah, Hira Ansar, Naif Almudawi, Abdulwahab Alazeb, Abdullah Alshahrani, Saud Alotaibi, and Ahmad Jalal. Smart home automation-based hand gesture recogni- tion using feature fusion and recurrent neural network. Sen- sors, 23:7523, 08 2023

    work page 2023

  7. [6]

    A low power, fully event-based gesture recognition system

    Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jef- frey McKinstry, Carmelo Di Nolfo, Tapan Nayak, Alexan- der Andreopoulos, Guillaume Garreau, Marcela Mendoza, Jeff Kusnitz, Michael Debole, Steve Esser, Tobi Delbruck, Myron Flickner, and Dharmendra Modha. A low power, fully event-based gesture recognition system. In 2017 IEEE Conference on Computer ...

    work page 2017

  8. [7]

    P. N. Arathi, S. Arthika, S. Ponmithra, K. Srinivasan, and V . Rukkumani. Gesture based home automation system. In 2017 International Conference on Nextgen Electronic Tech- nologies: Silicon to Software (ICNETS2) , pages 198–201, 2017

    work page 2017

Show all 67 references
  1. [8]

    Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions

    Sven Bambach, Stefan Lee, David Crandall, and Chen Yu. Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions. In IEEE International Conference on Computer Vision (ICCV), 2015

    2015

  2. [9]

    Ipn hand: A video dataset and benchmark for real-time continuous hand gesture recog- nition

    Gibran Benitez-Garcia, Jesus Olivares-Mercado, Gabriel Sanchez-Perez, and Keiji Yanai. Ipn hand: A video dataset and benchmark for real-time continuous hand gesture recog- nition. In 25th International Conference on Pattern Recogni- tion, ICPR 2020, Milan, Italy, Jan 10–15, 20...

    2020

  3. [10]

    C, K.V Suresh, Sheela S

    Dayananda Kumar N. C, K.V Suresh, Sheela S. J, and Di- nesh R. Sit-hands: A novel rgb-d dataset for static hand ges- ture recognition using kinect sensor. In 2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES), pages 1–6, 2023

    2023

  4. [11]

    Quo vadis, action recognition? a new model and the kinetics dataset

    Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017

    2017

  5. [12]

    Human computer interaction through hand gestures for home automation using microsoft kinect

    Smit Desai and Apurva Desai. Human computer interaction through hand gestures for home automation using microsoft kinect. In Nilesh Modi, Pramode Verma, and Bhushan Trivedi, editors, Proceedings of International Conference on Communication and Networks , pages 19–29, Singapore,

  6. [13]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition ...

    2010 arXiv

  7. [14]

    Pallab Jyoti Dutta, M

    H. Pallab Jyoti Dutta, M. K. Bhuyan, Debanga Raj Neog, Karl Fredric MacDorman, and Rabul Hussain Laskar. Patient assistance system based on hand gesture recognition. IEEE Transactions on Instrumentation and Measurement , 72:1– 13, 2023

    2023

  8. [15]

    Gestures for natu- ral interaction with video

    Nesrine Fourati and Emmanuel Marilly. Gestures for natu- ral interaction with video. In Visual Information Processing and Communication III, volume 8305, pages 170–181. SPIE, 2012

    2012

  9. [16]

    The role of gesture in communica- tion and thinking

    Susan Goldin-Meadow. The role of gesture in communica- tion and thinking. Trends in Cognitive Sciences, 3(11):419– 429, 1999

    1999

  10. [17]

    A preliminary study of iot-device control using gestures recognition

    Ayaka Hatori and Hiroyuki Kobayashi. A preliminary study of iot-device control using gestures recognition. In2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pages 976–979, 2017

    2017

  11. [19]

    Zhang, Shaoqing Ren, and Jian Sun

    Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR) , pages 770–778, 2015

    2016

  12. [20]

    Le, and Hartwig Adam

    Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V . Le, and Hartwig Adam. Searching for mobilenetv3. CoRR, abs/1905.02244, 2019

    1905 arXiv

  13. [21]

    Lared: a large rgb-d extensible hand gesture dataset

    Yuan-Sheng Hsiao, Jordi Sanchez-Riera, Tekoing Lim, Kai- Lung Hua, and Wen-Huang Cheng. Lared: a large rgb-d extensible hand gesture dataset. InACM SIGMM Conference on Multimedia Systems, 2014

    2014

  14. [22]

    Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen

    J. Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. ArXiv, abs/2106.09685, 2021

    2021 arXiv

  15. [24]

    A taxonomy of gestures in human computer interactions

    Maria Karam and m.c Schraefel. A taxonomy of gestures in human computer interactions. Electronics and Computer Science, 01 2005

    2005

  16. [25]

    Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation

    Kimmo Karkkainen and Jungseock Joo. Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1548–1558, 2021. 9

    2021

  17. [26]

    Electronic device control using hand gesture recognition system for dif- ferently abled

    Sanmukh Kaur, Anuranjana, and Nivedita Nair. Electronic device control using hand gesture recognition system for dif- ferently abled. In 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Conflu- ence), pages 371–375, 2018

    2018

  18. [27]

    Hand gesture recognition: A literature review

    Rafiqul Zaman Khan and Noor Ibraheem. Hand gesture recognition: A literature review. International Journal of Artificial Intelligence & Applications (IJAIA), 3:161–174, 08 2012

    2012

  19. [28]

    Real-time hand gesture detection and classification using convolutional neural networks

    Okan K ¨op¨ukl¨u, Ahmet Gunduz, Neslihan Kose, and Gerhard Rigoll. Real-time hand gesture detection and classification using convolutional neural networks. 2019 14th IEEE Inter- national Conference on Automatic Face & Gesture Recogni- tion (FG 2019), pages 1–8, 2019

    2019

  20. [29]

    Mivolo: Multi- input transformer for age and gender estimation

    Maksim Kuprashevich and Irina Tolstykh. Mivolo: Multi- input transformer for age and gender estimation. 2023

    2023

  21. [30]

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International Conference on Machine Learning, 2023

    2023

  22. [31]

    Detector-in-detector: Multi-level analysis for human-parts

    Xiaojie Li, Lu Yang, Qing Song, and Fuqiang Zhou. Detector-in-detector: Multi-level analysis for human-parts. arXiv preprint arXiv:****, 2019

    2019

  23. [32]

    Tsm: Temporal shift module for efficient video understanding

    Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 7082–7092, 2018

    2019

  24. [33]

    Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C

    Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference on Computer Vision, 2014

    2014

  25. [34]

    Learning discriminative representa- tions from rgb-d video data

    Li Liu and Ling Shao. Learning discriminative representa- tions from rgb-d video data. pages 1493–1500, 08 2013

    2013

  26. [35]

    Ssd: Single shot multibox detector

    Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander Berg. Ssd: Single shot multibox detector. volume 9905, pages 21– 37, 10 2016

    2016

  27. [36]

    A convnet for the 2020s

    Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feicht- enhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), 2022

    2022

  28. [37]

    Mediapipe: A framework for perceiving and processing reality

    Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris Mc- Clanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo- Ling Chang, Ming Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, and Matthias Grundmann. Mediapipe: A framework for perceiving and processing reality. In Thir...

    2019

  29. [38]

    Marin, F

    G. Marin, F. Dominio, and P. Zanuttigh. Hand gesture recog- nition with leap motion and kinect devices. In IEEE In- ternational Conference on Image Processing (ICIP) , Paris, France, 2014

    2014

  30. [39]

    Marin, F

    G. Marin, F. Dominio, and P. Zanuttigh. Hand gesture recog- nition with jointly calibrated leap motion and depth sensor. Multimedia Tools and Applications, 2015

    2015

  31. [40]

    Materzynska, G

    J. Materzynska, G. Berger, I. Bax, and R. Memisevic. The jester dataset: A large-scale video dataset of human gestures. IEEE/CVF (ICCVW), 2019

    2019

  32. [41]

    Ouhands database for hand detection and pose recog- nition

    Matti Matilainen, Pekka Sangi, Jukka Holappa, and Olli Silv´en. Ouhands database for hand detection and pose recog- nition. 2016 Sixth International Conference on Image Pro- cessing Theory, Tools and Applications (IPTA) , pages 1–5, 2016

    2016

  33. [42]

    A. Memo, L. Minto, and P. Zanuttigh. Exploiting silhouette descriptors and synthetic data for hand gesture recognition. STAG: Smart Tools Apps for Graphics, 2015

    2015

  34. [43]

    Memo and P

    A. Memo and P. Zanuttigh. Head-mounted gesture controlled interface for human-computer interaction. Multimedia Tools and Applications, 2017

    2017

  35. [44]

    Arpit Mittal, Andrew Zisserman, and Philip H. S. Torr. Hand detection using multiple proposals. In British Machine Vi- sion Conference, 2011

    2011

  36. [45]

    Hand gesture recognition with 3d convolutional neu- ral networks

    Pavlo Molchanov, Shalini Gupta, Kihwan Kim, and Jan Kautz. Hand gesture recognition with 3d convolutional neu- ral networks. 2015

    2015

  37. [46]

    Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural networks

    Pavlo Molchanov, Xiaodong Yang, Shalini Gupta, Kihwan Kim, Stephen Tyree, and Jan Kautz. Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pag...

    2016

  38. [47]

    Detecting hands and recognizing physical contact in the wild

    Supreeth Narasimhaswamy, Trung Nguyen, and Minh Hoai. Detecting hands and recognizing physical contact in the wild. In Advances in Neural Information Processing Sys- tems, 2020

    2020

  39. [48]

    Whose hands are these? hand detection and hand-body association in the wild

    Supreeth Narasimhaswamy, Thanh Nguyen, Mingzhen Huang, and Minh Hoai. Whose hands are these? hand detection and hand-body association in the wild. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4879–4889, 2022

    2022

  40. [49]

    Contextual attention for hand detection in the wild

    Supreeth Narasimhaswamy, Zhengwei Wei, Yang Wang, Justin Zhang, and Minh Hoai. Contextual attention for hand detection in the wild. 04 2019

    2019

  41. [50]

    Nuzzi, S

    C. Nuzzi, S. Pasinetti, R. Pagani, G. Coffetti, and G. Sansoni. Hands: an rgb-d dataset of static hand-gestures for human- robot interaction. Data in Brief, 35:106791, 2021

    2021

  42. [51]

    Alba-Castro

    Francisco Parada-Loira, Elisardo Gonz ´alez-Agulla, and Jos´e L. Alba-Castro. Hand gestures to control infotainment equipment in cars. In 2014 IEEE Intelligent Vehicles Sympo- sium Proceedings, pages 1–6, 2014

    2014

  43. [52]

    Spelling it out: Real- time asl fingerspelling recognition

    Nicolas Pugeault and Richard Bowden. Spelling it out: Real- time asl fingerspelling recognition. In 2011 IEEE Inter- national Conference on Computer Vision Workshops (ICCV Workshops), pages 1114–1119, 2011

    2011

  44. [53]

    Bhanu Prasad

    Jagdish Lal Raheja, Radhey Shyam, Umesh Kumar, and P. Bhanu Prasad. Real-time robotic hand control using hand gestures. In 2010 Second International Conference on Ma- chine Learning and Computing, pages 12–16, 2010

    2010

  45. [54]

    Robust hand gesture recognition based on finger-earth mover’s dis- tance with a commodity depth camera

    Zhou Ren, Junsong Yuan, and Zhengyou Zhang. Robust hand gesture recognition based on finger-earth mover’s dis- tance with a commodity depth camera. pages 1093–1096, 11 2011

    2011

  46. [55]

    Chairgest: A challenge for multimodal mid-air gesture recognition for close hci

    Simon Ruffieux, Denis Lalanne, and Elena Mugellini. Chairgest: A challenge for multimodal mid-air gesture recognition for close hci. pages 483–488, 12 2013. 10

    2013

  47. [56]

    Ram Pratap Sharma and Gyanendra K. Verma. Human com- puter interaction using hand gesture.Procedia Computer Sci- ence, 54:721–727, 2015. Eleventh International Conference on Communication Networks, ICCN 2015, August 21-23, 2015, Bangalore, India Eleventh International Conferen...

    2015

  48. [57]

    Tracking body and hands for gesture recognition: Natops aircraft han- dling signals database

    Yale Song, David Demirdjian, and Randall Davis. Tracking body and hands for gesture recognition: Natops aircraft han- dling signals database. In 2011 IEEE International Confer- ence on Automatic Face & Gesture Recognition (FG), pages 500–506, 2011

    2011

  49. [58]

    Designing hand gesture vocabularies for natural interaction by combining psycho-physiological and recognition factors

    Helman Stern, Juan Wachs, and Yaeledan. Designing hand gesture vocabularies for natural interaction by combining psycho-physiological and recognition factors. International Journal of Semantic Computing, 02, 11 2011

    2011

  50. [59]

    Learning spatiotemporal features with 3d convolutional networks

    Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spatiotemporal features with 3d convolutional networks. InProceedings of the IEEE Inter- national Conference on Computer Vision (ICCV), December 2015

    2015

  51. [60]

    Diffusers: State-of-the-art diffu- sion models

    Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, Dhruv Nair, Sayak Paul, William Berman, Yiyi Xu, Steven Liu, and Thomas Wolf. Diffusers: State-of-the-art diffu- sion models. https://github.com/huggingface/ diffusers, 2022

    2022

  52. [61]

    Li, Yibing Zhao, Shuai Zhou, Isabelle Guyon, and Sergio Escalera

    Jun Wan, Stan Z. Li, Yibing Zhao, Shuai Zhou, Isabelle Guyon, and Sergio Escalera. Chalearn looking at people rgb- d isolated and continuous datasets for gesture recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 761–769, 2016

    2016

  53. [62]

    Yolov10: Real-time end- to-end object detection

    Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jun- gong Han, and Guiguang Ding. Yolov10: Real-time end- to-end object detection. arXiv preprint arXiv:2405.14458 , 2024

    2024 arXiv

  54. [63]

    Mems devices-based hand gesture recognition via wearable computing

    Huihui Wang, Bo Ru, Xin Miao, Qin Gao, Masood Habib, Long Liu, and Sen Qiu. Mems devices-based hand gesture recognition via wearable computing. Micromachines, 14, 04 2023

    2023

  55. [64]

    Smart control of home appliances using hand gesture recog- nition in an iot-enabled system

    Chih-Hsiang Yang, Yi-Nan Lin, Sheng-Kuan Wang, Victor Shen, Yi-Chih Tung, Frank Shen, and Chun-Hsiang Huang. Smart control of home appliances using hand gesture recog- nition in an iot-enabled system. Applied Artificial Intelli- gence, 37, 02 2023

    2023

  56. [65]

    Mais Yasen and Shaidah S. Jusoh. A systematic review on hand gesture recognition techniques, challenges and applica- tions. PeerJ Computer Science, 5, 2019

    2019

  57. [66]

    Zoomtouch: Multi-user remote robot control in zoom by dnn-based ges- ture recognition

    Ilya Zakharkin, Arman Tsaturyan, Miguel Altamirano, Jonathan Tirado, and Dzmitry Tsetserukou. Zoomtouch: Multi-user remote robot control in zoom by dnn-based ges- ture recognition. 11 2020

    2020

  58. [67]

    Zhang, C

    Y . Zhang, C. Cao, J. Cheng, and H. Lu. Egogesture: A new dataset and benchmark for egocentric hand gesture recognition. IEEE Transactions on Multimedia (T-MM) , 20(5):1038–1050, 2018

    2018

  59. [68]

    click” and “double-click

    Hongyang Zhao, Shuangquan Wang, Gang Zhou, and Daqing Zhang. Gesture-enabled remote control for health- care. In 2017 IEEE/ACM International Conference on Con- nected Health: Applications, Systems and Engineering Tech- nologies (CHASE), pages 392–401, 2017. 11 Supplementary ma...

    2017

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.